Advanced technologies (e.g. AI, machine learning) are slowly permeating the real estate industry which is allowing owner/operators, investors, agents/brokers and other end-users of real estate to operate in a more efficient real estate marketplace. Consumers who are searching for their “dream-home” can use PropTech applications to review customized floor plans of home models, ideally in 3D. Owner/operators who seek to push NOI for their portfolio are now taking a data-first approach to manage operational workflows such as energy management, maintenance & repairs as well as CapEx forecasting. Other PropTech applications are leveraging big data to analyze locations and properties in a specific real estate market, while providing advanced information about specific properties. While there are many other PropTech start-ups that are pushing the real estate market to new heights, we are still in the early innings.
5:00 PM | REGISTRATION & CHECK-IN
5:30 PM | OPENING REMARKS
Patrick Nessenthaler, CFA, CAIA, Vice President, Real Estate Industries, Institutional Markets, MUFG
5:35 PM | KEYNOTE ADDRESS
Dr. Mehrzad Mahdavi, Executive Director, FDP (Financial Data Professional) Institute
6:20 PM | PANEL
Andrea Jang, COO, Ackman-Ziff
Momei Qu, Senior Vice President, PSP Growth
Zak Schwarzman, General Partner, MetaProp
7:10 PM | Q & A
7:25 PM | CLOSING REMARKS
7:30 PM | NETWORKING & CATERED RECEPTION
- The digitization of real estate will only be as revolutionary as its implementation and execution. We will explore the foundational infrastructure needed for real estate companies to adopt a new digital ecosystem. How can these companies hire the right talent? We will address these questions and other challenges in building in-house teams versus partnerships / investments in data science led startups in the PropTech space.
- Commercial real estate is a relatively illiquid asset class; how can the industry utilize AI, machine learning and big data to amplify the valuation industry (e.g. hedonic pricing models, which can be updated in real-time, that produce better distributions-to-fit, r-squares)?
- By 2030, Artificial Intelligence is predicted to add $15 trillion to global GDP, according to PwC. The real estate industry shows significant opportunities to capture a portion of this growth. Real estate companies are utilizing AI to create a more personalized experience for transactions in home-buying and mortgage lending, for example, to create a more efficient end-to-end experience. This information is being used to forecast potential investment sales or calibrate locate rental rates. We will explore additional applications on how the real estate industry can explore leveraging AI further.
- Tail Risk, which is amplified via securitizations and other structured products, continues to be problematic for investors in alternative asset classes (e.g. real estate mortgage-backed securities), how can our industry utilize new technology to measure tail risk more accurately?
- How can investors synthesize millions of data-points ranging from non-traditional data-sources (e.g. mobile phone signal patterns and Yelp reviews) and traditional data-sources (e.g. area’s crime rate and median household income) with PropTech applications in order to find the next neighborhood “hot-spot” worthy of investment?
- The traditional 60/40 Stock/Bond Portfolio is dead. Adding alternative asset classes to the traditional portfolio enables investors to enhance the diversification and lower the correlation of their portfolio, adding a more stable yield to their portfolio (e.g. infrastructure), hedge crisis risk (e.g. CTA’s) and other diversifiers and/or yield enhancers. However, these types of investment vehicles are not usually accessible to retailers. How can new PropTech applications bring direct real estate investment and other real assets to the retail community?